Flow Cytometry Gate Transfer Using Sample Image Morphing
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Solution Overview
Problem
Creating and modifying gates for cytometry data, especially in complex samples with multiple labels, is tedious and time-consuming, leading to intra- and inter-experimental variability.
Innovation Solution
A method for automatically adjusting gates by generating images of training and target samples, selecting a similar training sample, calculating a transformation, and applying it to the training gate to generate a target sample gate using techniques like non-rigid deformable registration and Gaussian blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating is used for complex cytometry samples with multiple labels, then the operator can identify specific cell populations, but the process becomes tedious and time-consuming
Solution Approach 1:
The system copies gating information from reference samples to new samples. Training samples with manually created gates serve as templates, and the algorithm automatically copies and adapts these gates to new samples based on similarity matching, eliminating the need for manual gate creation in each new sample
Solution Approach 2:
The system performs preliminary gating on training samples before actual analysis. By pre-creating gates on representative training samples and storing them for automatic retrieval, the system prepares gating solutions in advance that can be quickly applied to new samples without time-consuming manual intervention
2Measurement precision
If manual gating is performed for each sample, then specific cell populations can be identified, but intra- and inter-experimental variability increases
Solution Approach 1:
By copying gates from reference training samples to new samples, the system ensures consistent gating criteria across all experiments. This eliminates operator-specific variations and ensures that the same gating logic is applied uniformly, reducing both intra- and inter-experimental variability
Solution Approach 2:
The system transforms gates based on sample-specific parameters while maintaining the underlying gating logic. By adjusting gate parameters automatically based on sample characteristics rather than manual re-creation, the system maintains consistency in gating methodology while adapting to sample variations
3Productivity
If automated gating methods are implemented, then time consumption is reduced, but the complexity of the system increases
Solution Approach 1:
The system uses simple image copying and transformation operations rather than complex machine learning models. By representing cytometry data as images and using image processing techniques for matching and transformation, the system achieves automation with relatively simple computational methods
Solution Approach 2:
The system introduces image representation as an intermediary between raw cytometry data and gating results. By converting data to images, applying similarity matching, and then transforming gates back, the system creates a straightforward automated pipeline without requiring complex direct algorithms
Data Source
AI summary
A method for automatically adjusting gates to cytometry data of a target sample is provided. The method includes receiving training cytometry data corresponding to a plurality of training samples; generating an image of the target sample, generating images of the plurality of training samples; selecting a training sample from the plurality of training samples based on a similarity between the image of the selected training sample and the image of the target sample; calculating a transformation required to morph between the image of the selected training sample and the image of the target sample; and generating a target sample gate by applying the calculated transformation to the training gate associated with the selected training sample. A corresponding system and computer-readable medium are also provided.